Overview

Dataset statistics

Number of variables20
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory163.7 B

Variable types

Text1
Numeric19

Alerts

% of Total Population is highly overall correlated with Area (km*km) and 15 other fieldsHigh correlation
Area (km*km) is highly overall correlated with % of Total Population and 15 other fieldsHigh correlation
Arson is highly overall correlated with % of Total Population and 15 other fieldsHigh correlation
Assault on women is highly overall correlated with % of Total Population and 14 other fieldsHigh correlation
Dacoity is highly overall correlated with % of Total Population and 14 other fieldsHigh correlation
Females is highly overall correlated with % of Total Population and 15 other fieldsHigh correlation
Hurt is highly overall correlated with % of Total Population and 15 other fieldsHigh correlation
Kidnapping and Abduction is highly overall correlated with % of Total Population and 14 other fieldsHigh correlation
Literacy Rate (%) is highly overall correlated with % of Total Population and 7 other fieldsHigh correlation
Males is highly overall correlated with % of Total Population and 15 other fieldsHigh correlation
Murder is highly overall correlated with % of Total Population and 15 other fieldsHigh correlation
Other Crimes Against SCs is highly overall correlated with % of Total Population and 14 other fieldsHigh correlation
Population is highly overall correlated with % of Total Population and 15 other fieldsHigh correlation
Prevention of atrocities (POA) Act is highly overall correlated with % of Total Population and 14 other fieldsHigh correlation
Protection of Civil Rights (PCR) Act is highly overall correlated with % of Total Population and 14 other fieldsHigh correlation
Robbery is highly overall correlated with % of Total Population and 14 other fieldsHigh correlation
Total_Crimes is highly overall correlated with % of Total Population and 14 other fieldsHigh correlation
State has unique valuesUnique
Population has unique valuesUnique
Males has unique valuesUnique
Females has unique valuesUnique
Literacy Rate (%) has unique valuesUnique
Area (km*km) has unique valuesUnique
Murder has 9 (25.7%) zerosZeros
Assault on women has 10 (28.6%) zerosZeros
Kidnapping and Abduction has 10 (28.6%) zerosZeros
Dacoity has 19 (54.3%) zerosZeros
Robbery has 13 (37.1%) zerosZeros
Arson has 14 (40.0%) zerosZeros
Hurt has 11 (31.4%) zerosZeros
Prevention of atrocities (POA) Act has 9 (25.7%) zerosZeros
Protection of Civil Rights (PCR) Act has 13 (37.1%) zerosZeros
Other Crimes Against SCs has 6 (17.1%) zerosZeros
Total_Crimes has 4 (11.4%) zerosZeros

Reproduction

Analysis started2024-01-01 16:22:21.743566
Analysis finished2024-01-01 16:23:07.300881
Duration45.56 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

State
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size408.0 B
2024-01-01T21:53:07.444628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length14
Mean length10.285714
Min length3

Characters and Unicode

Total characters360
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st rowAndhra Pradesh
2nd rowArunachal Pradesh
3rd rowAssam
4th rowBihar
5th rowChandigarh
ValueCountFrequency (%)
pradesh 5
 
9.6%
and 4
 
7.7%
delhi 1
 
1.9%
mizoram 1
 
1.9%
manipur 1
 
1.9%
arunachal 1
 
1.9%
gujarat 1
 
1.9%
assam 1
 
1.9%
bihar 1
 
1.9%
chandigarh 1
 
1.9%
Other values (35) 35
67.3%
2024-01-01T21:53:08.000804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 76
21.1%
r 29
 
8.1%
h 27
 
7.5%
d 22
 
6.1%
n 20
 
5.6%
17
 
4.7%
i 16
 
4.4%
s 16
 
4.4%
e 14
 
3.9%
t 11
 
3.1%
Other values (33) 112
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 295
81.9%
Uppercase Letter 48
 
13.3%
Space Separator 17
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 76
25.8%
r 29
 
9.8%
h 27
 
9.2%
d 22
 
7.5%
n 20
 
6.8%
i 16
 
5.4%
s 16
 
5.4%
e 14
 
4.7%
t 11
 
3.7%
m 10
 
3.4%
Other values (13) 54
18.3%
Uppercase Letter
ValueCountFrequency (%)
P 7
14.6%
M 5
10.4%
A 4
 
8.3%
N 4
 
8.3%
D 4
 
8.3%
K 3
 
6.2%
H 3
 
6.2%
J 2
 
4.2%
C 2
 
4.2%
G 2
 
4.2%
Other values (9) 12
25.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 343
95.3%
Common 17
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 76
22.2%
r 29
 
8.5%
h 27
 
7.9%
d 22
 
6.4%
n 20
 
5.8%
i 16
 
4.7%
s 16
 
4.7%
e 14
 
4.1%
t 11
 
3.2%
m 10
 
2.9%
Other values (32) 102
29.7%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 76
21.1%
r 29
 
8.1%
h 27
 
7.5%
d 22
 
6.1%
n 20
 
5.6%
17
 
4.7%
i 16
 
4.4%
s 16
 
4.4%
e 14
 
3.9%
t 11
 
3.1%
Other values (33) 112
31.1%

Population
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34592027
Minimum64473
Maximum1.9981234 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:08.223240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64473
5-th percentile313570.4
Q11421136
median16787941
Q360767494
95-th percentile1.0658192 × 108
Maximum1.9981234 × 108
Range1.9974787 × 108
Interquartile range (IQR)59346358

Descriptive statistics

Standard deviation44455165
Coefficient of variation (CV)1.2851275
Kurtosis4.3219862
Mean34592027
Median Absolute Deviation (MAD)16177364
Skewness1.8598235
Sum1.2107209 × 109
Variance1.9762617 × 1015
MonotonicityNot monotonic
2024-01-01T21:53:08.457659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
84580777 1
 
2.9%
72147030 1
 
2.9%
1978502 1
 
2.9%
41974218 1
 
2.9%
1247953 1
 
2.9%
27743338 1
 
2.9%
68548437 1
 
2.9%
610577 1
 
2.9%
3673917 1
 
2.9%
2966889 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
64473 1
2.9%
243247 1
2.9%
343709 1
2.9%
380581 1
2.9%
610577 1
2.9%
1055450 1
2.9%
1097206 1
2.9%
1247953 1
2.9%
1383727 1
2.9%
1458545 1
2.9%
ValueCountFrequency (%)
199812341 1
2.9%
112374333 1
2.9%
104099452 1
2.9%
91276115 1
2.9%
84580777 1
2.9%
72626809 1
2.9%
72147030 1
2.9%
68548437 1
2.9%
61095297 1
2.9%
60439692 1
2.9%

% of Total Population
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8568571
Minimum0.01
Maximum16.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:08.666348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.027
Q10.115
median1.39
Q35.02
95-th percentile8.804
Maximum16.5
Range16.49
Interquartile range (IQR)4.905

Descriptive statistics

Standard deviation3.6719427
Coefficient of variation (CV)1.2853085
Kurtosis4.3148657
Mean2.8568571
Median Absolute Deviation (MAD)1.33
Skewness1.8585872
Sum99.99
Variance13.483163
MonotonicityNot monotonic
2024-01-01T21:53:08.858938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.03 2
 
5.7%
0.09 2
 
5.7%
6.99 1
 
2.9%
0.05 1
 
2.9%
0.16 1
 
2.9%
3.47 1
 
2.9%
0.1 1
 
2.9%
2.29 1
 
2.9%
5.66 1
 
2.9%
0.3 1
 
2.9%
Other values (23) 23
65.7%
ValueCountFrequency (%)
0.01 1
2.9%
0.02 1
2.9%
0.03 2
5.7%
0.05 1
2.9%
0.09 2
5.7%
0.1 1
2.9%
0.11 1
2.9%
0.12 1
2.9%
0.16 1
2.9%
0.21 1
2.9%
ValueCountFrequency (%)
16.5 1
2.9%
9.28 1
2.9%
8.6 1
2.9%
7.54 1
2.9%
6.99 1
2.9%
6 1
2.9%
5.96 1
2.9%
5.66 1
2.9%
5.05 1
2.9%
4.99 1
2.9%

Males
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17800624
Minimum33123
Maximum1.0448051 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:09.085462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33123
5-th percentile180722.3
Q1726526
median8887326
Q331228958
95-th percentile55467627
Maximum1.0448051 × 108
Range1.0444739 × 108
Interquartile range (IQR)30502432

Descriptive statistics

Standard deviation23055506
Coefficient of variation (CV)1.2952077
Kurtosis4.6276226
Mean17800624
Median Absolute Deviation (MAD)8306663
Skewness1.9146508
Sum6.2302184 × 108
Variance5.3155637 × 1014
MonotonicityNot monotonic
2024-01-01T21:53:09.346560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
42442146 1
 
2.9%
36137975 1
 
2.9%
1024649 1
 
2.9%
21212136 1
 
2.9%
612511 1
 
2.9%
14639465 1
 
2.9%
35550997 1
 
2.9%
323070 1
 
2.9%
1874376 1
 
2.9%
1491832 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
33123 1
2.9%
150301 1
2.9%
193760 1
2.9%
202871 1
2.9%
323070 1
2.9%
555339 1
2.9%
580663 1
2.9%
612511 1
2.9%
713912 1
2.9%
739140 1
2.9%
ValueCountFrequency (%)
104480510 1
2.9%
58243056 1
2.9%
54278157 1
2.9%
46809027 1
2.9%
42442146 1
2.9%
37612306 1
2.9%
36137975 1
2.9%
35550997 1
2.9%
31491260 1
2.9%
30966657 1
2.9%

Females
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16784221
Minimum31350
Maximum95331831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:09.558117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31350
5-th percentile132848.1
Q1694610
median7800615
Q329538536
95-th percentile51114290
Maximum95331831
Range95300481
Interquartile range (IQR)28843926

Descriptive statistics

Standard deviation21413558
Coefficient of variation (CV)1.2758148
Kurtosis3.9926285
Mean16784221
Median Absolute Deviation (MAD)7513108
Skewness1.8006592
Sum5.8744773 × 108
Variance4.5854046 × 1014
MonotonicityNot monotonic
2024-01-01T21:53:09.761610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
42138631 1
 
2.9%
36009055 1
 
2.9%
953853 1
 
2.9%
20762082 1
 
2.9%
635442 1
 
2.9%
13103873 1
 
2.9%
32997440 1
 
2.9%
287507 1
 
2.9%
1799541 1
 
2.9%
1475057 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
31350 1
2.9%
92946 1
2.9%
149949 1
2.9%
177710 1
2.9%
287507 1
2.9%
474787 1
2.9%
541867 1
2.9%
635442 1
2.9%
669815 1
2.9%
719405 1
2.9%
ValueCountFrequency (%)
95331831 1
2.9%
54131277 1
2.9%
49821295 1
2.9%
44467088 1
2.9%
42138631 1
2.9%
36009055 1
2.9%
35014503 1
2.9%
32997440 1
2.9%
30128640 1
2.9%
28948432 1
2.9%

Sex Ratio
Real number (ℝ)

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean931.22857
Minimum618
Maximum1084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:09.943852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum618
5-th percentile804.8
Q1903.5
median946
Q3974.5
95-th percentile1008.3
Maximum1084
Range466
Interquartile range (IQR)71

Descriptive statistics

Standard deviation79.883402
Coefficient of variation (CV)0.085782809
Kurtosis6.3699564
Mean931.22857
Median Absolute Deviation (MAD)33
Skewness-1.8632571
Sum32593
Variance6381.358
MonotonicityNot monotonic
2024-01-01T21:53:10.161552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
931 2
 
5.7%
973 2
 
5.7%
993 1
 
2.9%
960 1
 
2.9%
1037 1
 
2.9%
895 1
 
2.9%
928 1
 
2.9%
890 1
 
2.9%
996 1
 
2.9%
912 1
 
2.9%
Other values (23) 23
65.7%
ValueCountFrequency (%)
618 1
2.9%
774 1
2.9%
818 1
2.9%
868 1
2.9%
876 1
2.9%
879 1
2.9%
889 1
2.9%
890 1
2.9%
895 1
2.9%
912 1
2.9%
ValueCountFrequency (%)
1084 1
2.9%
1037 1
2.9%
996 1
2.9%
993 1
2.9%
992 1
2.9%
991 1
2.9%
989 1
2.9%
979 1
2.9%
976 1
2.9%
973 2
5.7%

Literacy Rate (%)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.940286
Minimum61.8
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:10.352072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61.8
5-th percentile65.891
Q171.235
median78.03
Q385.95
95-th percentile91.486
Maximum94
Range32.2
Interquartile range (IQR)14.715

Descriptive statistics

Standard deviation8.5988373
Coefficient of variation (CV)0.11032597
Kurtosis-0.95077088
Mean77.940286
Median Absolute Deviation (MAD)7.82
Skewness0.0022796746
Sum2727.91
Variance73.940003
MonotonicityNot monotonic
2024-01-01T21:53:10.570521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
67.02 1
 
2.9%
80.09 1
 
2.9%
79.55 1
 
2.9%
72.87 1
 
2.9%
85.85 1
 
2.9%
75.84 1
 
2.9%
66.11 1
 
2.9%
81.42 1
 
2.9%
87.22 1
 
2.9%
74.43 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
61.8 1
2.9%
65.38 1
2.9%
66.11 1
2.9%
66.41 1
2.9%
67.02 1
2.9%
67.16 1
2.9%
67.68 1
2.9%
69.32 1
2.9%
70.28 1
2.9%
72.19 1
2.9%
ValueCountFrequency (%)
94 1
2.9%
91.85 1
2.9%
91.33 1
2.9%
88.7 1
2.9%
87.22 1
2.9%
87.1 1
2.9%
86.63 1
2.9%
86.21 1
2.9%
86.05 1
2.9%
85.85 1
2.9%

Area (km*km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93921.171
Minimum32
Maximum342239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:10.770019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile113.4
Q19367.5
median53483
Q3145449
95-th percentile307872.6
Maximum342239
Range342207
Interquartile range (IQR)136081.5

Descriptive statistics

Standard deviation103754.36
Coefficient of variation (CV)1.1046962
Kurtosis0.0043175737
Mean93921.171
Median Absolute Deviation (MAD)49781
Skewness1.0873597
Sum3287241
Variance1.0764968 × 1010
MonotonicityNot monotonic
2024-01-01T21:53:10.978494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
275045 1
 
2.9%
130058 1
 
2.9%
16579 1
 
2.9%
155707 1
 
2.9%
479 1
 
2.9%
50362 1
 
2.9%
342239 1
 
2.9%
7096 1
 
2.9%
10486 1
 
2.9%
22429 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
32 1
2.9%
112 1
2.9%
114 1
2.9%
479 1
2.9%
491 1
2.9%
1484 1
2.9%
3702 1
2.9%
7096 1
2.9%
8249 1
2.9%
10486 1
2.9%
ValueCountFrequency (%)
342239 1
2.9%
308245 1
2.9%
307713 1
2.9%
275045 1
2.9%
240928 1
2.9%
222236 1
2.9%
196024 1
2.9%
191791 1
2.9%
155707 1
2.9%
135191 1
2.9%

Density (1/km*km)
Real number (ℝ)

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1091.8857
Minimum17
Maximum11297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:11.185040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile50.2
Q1160.5
median350
Q3763
95-th percentile4594.2
Maximum11297
Range11280
Interquartile range (IQR)602.5

Descriptive statistics

Standard deviation2385.4582
Coefficient of variation (CV)2.1847142
Kurtosis13.300037
Mean1091.8857
Median Absolute Deviation (MAD)227
Skewness3.6659287
Sum38216
Variance5690410.7
MonotonicityNot monotonic
2024-01-01T21:53:11.381650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
308 2
 
5.7%
189 2
 
5.7%
555 1
 
2.9%
119 1
 
2.9%
269 1
 
2.9%
2598 1
 
2.9%
550 1
 
2.9%
201 1
 
2.9%
86 1
 
2.9%
350 1
 
2.9%
Other values (23) 23
65.7%
ValueCountFrequency (%)
17 1
2.9%
46 1
2.9%
52 1
2.9%
56 1
2.9%
86 1
2.9%
119 1
2.9%
122 1
2.9%
123 1
2.9%
132 1
2.9%
189 2
5.7%
ValueCountFrequency (%)
11297 1
2.9%
9252 1
2.9%
2598 1
2.9%
2169 1
2.9%
2013 1
2.9%
1102 1
2.9%
1030 1
2.9%
859 1
2.9%
828 1
2.9%
698 1
2.9%

Murder
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.71429
Minimum0
Maximum3577
Zeros9
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:11.568151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median12
Q3181
95-th percentile759.6
Maximum3577
Range3577
Interquartile range (IQR)180.5

Descriptive statistics

Standard deviation625.69776
Coefficient of variation (CV)2.7720787
Kurtosis25.729487
Mean225.71429
Median Absolute Deviation (MAD)12
Skewness4.8514547
Sum7900
Variance391497.68
MonotonicityNot monotonic
2024-01-01T21:53:11.744240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 9
25.7%
4 2
 
5.7%
1 2
 
5.7%
556 1
 
2.9%
12 1
 
2.9%
2 1
 
2.9%
50 1
 
2.9%
3577 1
 
2.9%
352 1
 
2.9%
8 1
 
2.9%
Other values (15) 15
42.9%
ValueCountFrequency (%)
0 9
25.7%
1 2
 
5.7%
2 1
 
2.9%
3 1
 
2.9%
4 2
 
5.7%
8 1
 
2.9%
10 1
 
2.9%
12 1
 
2.9%
32 1
 
2.9%
36 1
 
2.9%
ValueCountFrequency (%)
3577 1
2.9%
1062 1
2.9%
630 1
2.9%
556 1
2.9%
352 1
2.9%
302 1
2.9%
286 1
2.9%
243 1
2.9%
204 1
2.9%
158 1
2.9%

Assault on women
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.77143
Minimum0
Maximum4107
Zeros10
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:11.925962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median57
Q3388
95-th percentile2423.8
Maximum4107
Range4107
Interquartile range (IQR)388

Descriptive statistics

Standard deviation948.53736
Coefficient of variation (CV)2.0857453
Kurtosis9.0484407
Mean454.77143
Median Absolute Deviation (MAD)57
Skewness3.0084463
Sum15917
Variance899723.12
MonotonicityNot monotonic
2024-01-01T21:53:12.106018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 10
28.6%
1110 1
 
2.9%
19 1
 
2.9%
65 1
 
2.9%
3625 1
 
2.9%
15 1
 
2.9%
259 1
 
2.9%
17 1
 
2.9%
1909 1
 
2.9%
115 1
 
2.9%
Other values (16) 16
45.7%
ValueCountFrequency (%)
0 10
28.6%
1 1
 
2.9%
2 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
15 1
 
2.9%
17 1
 
2.9%
19 1
 
2.9%
57 1
 
2.9%
65 1
 
2.9%
ValueCountFrequency (%)
4107 1
2.9%
3625 1
2.9%
1909 1
2.9%
1110 1
2.9%
979 1
2.9%
782 1
2.9%
661 1
2.9%
574 1
2.9%
447 1
2.9%
329 1
2.9%

Kidnapping and Abduction
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.65714
Minimum0
Maximum2225
Zeros10
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:12.268981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q3111.5
95-th percentile408.5
Maximum2225
Range2225
Interquartile range (IQR)111.5

Descriptive statistics

Standard deviation383.30536
Coefficient of variation (CV)2.8678255
Kurtosis27.863981
Mean133.65714
Median Absolute Deviation (MAD)8
Skewness5.0799303
Sum4678
Variance146923
MonotonicityNot monotonic
2024-01-01T21:53:12.432034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 10
28.6%
1 4
 
11.4%
2 2
 
5.7%
238 1
 
2.9%
151 1
 
2.9%
2225 1
 
2.9%
6 1
 
2.9%
62 1
 
2.9%
8 1
 
2.9%
365 1
 
2.9%
Other values (12) 12
34.3%
ValueCountFrequency (%)
0 10
28.6%
1 4
 
11.4%
2 2
 
5.7%
6 1
 
2.9%
8 1
 
2.9%
18 1
 
2.9%
23 1
 
2.9%
27 1
 
2.9%
43 1
 
2.9%
44 1
 
2.9%
ValueCountFrequency (%)
2225 1
2.9%
510 1
2.9%
365 1
2.9%
301 1
2.9%
238 1
2.9%
221 1
2.9%
151 1
2.9%
135 1
2.9%
127 1
2.9%
96 1
2.9%

Dacoity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.285714
Minimum0
Maximum122
Zeros19
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:12.589292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile64
Maximum122
Range122
Interquartile range (IQR)9

Descriptive statistics

Standard deviation25.930418
Coefficient of variation (CV)2.2976319
Kurtosis10.988821
Mean11.285714
Median Absolute Deviation (MAD)0
Skewness3.2494927
Sum395
Variance672.38655
MonotonicityNot monotonic
2024-01-01T21:53:12.753182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 19
54.3%
1 3
 
8.6%
9 2
 
5.7%
5 2
 
5.7%
14 1
 
2.9%
29 1
 
2.9%
85 1
 
2.9%
8 1
 
2.9%
16 1
 
2.9%
17 1
 
2.9%
Other values (3) 3
 
8.6%
ValueCountFrequency (%)
0 19
54.3%
1 3
 
8.6%
5 2
 
5.7%
8 1
 
2.9%
9 2
 
5.7%
14 1
 
2.9%
16 1
 
2.9%
17 1
 
2.9%
18 1
 
2.9%
29 1
 
2.9%
ValueCountFrequency (%)
122 1
2.9%
85 1
2.9%
55 1
2.9%
29 1
2.9%
18 1
2.9%
17 1
2.9%
16 1
2.9%
14 1
2.9%
9 2
5.7%
8 1
2.9%

Robbery
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.228571
Minimum0
Maximum214
Zeros13
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:12.912304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q326
95-th percentile132.1
Maximum214
Range214
Interquartile range (IQR)26

Descriptive statistics

Standard deviation54.064823
Coefficient of variation (CV)1.9855916
Kurtosis6.9808741
Mean27.228571
Median Absolute Deviation (MAD)1
Skewness2.6601387
Sum953
Variance2923.005
MonotonicityNot monotonic
2024-01-01T21:53:13.067853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 13
37.1%
1 5
 
14.3%
33 2
 
5.7%
214 2
 
5.7%
39 1
 
2.9%
9 1
 
2.9%
4 1
 
2.9%
67 1
 
2.9%
97 1
 
2.9%
96 1
 
2.9%
Other values (7) 7
20.0%
ValueCountFrequency (%)
0 13
37.1%
1 5
 
14.3%
2 1
 
2.9%
4 1
 
2.9%
6 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
13 1
 
2.9%
15 1
 
2.9%
19 1
 
2.9%
ValueCountFrequency (%)
214 2
5.7%
97 1
2.9%
96 1
2.9%
77 1
2.9%
67 1
2.9%
39 1
2.9%
33 2
5.7%
19 1
2.9%
15 1
2.9%
13 1
2.9%

Arson
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.628571
Minimum0
Maximum715
Zeros14
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:13.222955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q360.5
95-th percentile456.5
Maximum715
Range715
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation163.5791
Coefficient of variation (CV)2.1072023
Kurtosis7.1871044
Mean77.628571
Median Absolute Deviation (MAD)1
Skewness2.692589
Sum2717
Variance26758.123
MonotonicityNot monotonic
2024-01-01T21:53:13.388236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 14
40.0%
1 4
 
11.4%
136 1
 
2.9%
446 1
 
2.9%
715 1
 
2.9%
89 1
 
2.9%
2 1
 
2.9%
481 1
 
2.9%
170 1
 
2.9%
109 1
 
2.9%
Other values (9) 9
25.7%
ValueCountFrequency (%)
0 14
40.0%
1 4
 
11.4%
2 1
 
2.9%
4 1
 
2.9%
17 1
 
2.9%
20 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
25 1
 
2.9%
32 1
 
2.9%
ValueCountFrequency (%)
715 1
2.9%
481 1
2.9%
446 1
2.9%
309 1
2.9%
170 1
2.9%
136 1
2.9%
115 1
2.9%
109 1
2.9%
89 1
2.9%
32 1
2.9%

Hurt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1404.4
Minimum0
Maximum9993
Zeros11
Zeros (%)31.4%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:13.567739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median64
Q31981
95-th percentile6363.2
Maximum9993
Range9993
Interquartile range (IQR)1981

Descriptive statistics

Standard deviation2427.873
Coefficient of variation (CV)1.7287618
Kurtosis4.1800846
Mean1404.4
Median Absolute Deviation (MAD)64
Skewness2.0934503
Sum49154
Variance5894567.4
MonotonicityNot monotonic
2024-01-01T21:53:14.122481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 11
31.4%
7248 1
 
2.9%
766 1
 
2.9%
26 1
 
2.9%
160 1
 
2.9%
4889 1
 
2.9%
64 1
 
2.9%
2269 1
 
2.9%
39 1
 
2.9%
5984 1
 
2.9%
Other values (15) 15
42.9%
ValueCountFrequency (%)
0 11
31.4%
1 1
 
2.9%
5 1
 
2.9%
7 1
 
2.9%
11 1
 
2.9%
26 1
 
2.9%
39 1
 
2.9%
64 1
 
2.9%
134 1
 
2.9%
160 1
 
2.9%
ValueCountFrequency (%)
9993 1
2.9%
7248 1
2.9%
5984 1
2.9%
4889 1
2.9%
4524 1
2.9%
3281 1
2.9%
3271 1
2.9%
2269 1
2.9%
2023 1
2.9%
1939 1
2.9%

Prevention of atrocities (POA) Act
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3558.8286
Minimum0
Maximum26378
Zeros9
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:14.282690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median256
Q33032.5
95-th percentile17639.5
Maximum26378
Range26378
Interquartile range (IQR)3032

Descriptive statistics

Standard deviation6726.3383
Coefficient of variation (CV)1.8900428
Kurtosis4.6655657
Mean3558.8286
Median Absolute Deviation (MAD)256
Skewness2.2680443
Sum124559
Variance45243626
MonotonicityNot monotonic
2024-01-01T21:53:14.465755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 9
25.7%
3 2
 
5.7%
15160 1
 
2.9%
2855 1
 
2.9%
111 1
 
2.9%
619 1
 
2.9%
26378 1
 
2.9%
8 1
 
2.9%
10845 1
 
2.9%
7939 1
 
2.9%
Other values (16) 16
45.7%
ValueCountFrequency (%)
0 9
25.7%
1 1
 
2.9%
3 2
 
5.7%
6 1
 
2.9%
8 1
 
2.9%
12 1
 
2.9%
17 1
 
2.9%
111 1
 
2.9%
256 1
 
2.9%
619 1
 
2.9%
ValueCountFrequency (%)
26378 1
2.9%
23425 1
2.9%
15160 1
2.9%
13773 1
2.9%
10845 1
2.9%
9711 1
2.9%
7939 1
2.9%
3971 1
2.9%
3210 1
2.9%
2855 1
2.9%

Protection of Civil Rights (PCR) Act
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122
Minimum0
Maximum1511
Zeros13
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:14.631879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q329.5
95-th percentile500.3
Maximum1511
Range1511
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation292.79575
Coefficient of variation (CV)2.3999652
Kurtosis14.878508
Mean122
Median Absolute Deviation (MAD)4
Skewness3.5417233
Sum4270
Variance85729.353
MonotonicityNot monotonic
2024-01-01T21:53:14.805469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 13
37.1%
1 3
 
8.6%
11 2
 
5.7%
1511 1
 
2.9%
4 1
 
2.9%
500 1
 
2.9%
7 1
 
2.9%
5 1
 
2.9%
231 1
 
2.9%
18 1
 
2.9%
Other values (10) 10
28.6%
ValueCountFrequency (%)
0 13
37.1%
1 3
 
8.6%
3 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
11 2
 
5.7%
18 1
 
2.9%
19 1
 
2.9%
ValueCountFrequency (%)
1511 1
2.9%
501 1
2.9%
500 1
2.9%
482 1
2.9%
466 1
2.9%
378 1
2.9%
231 1
2.9%
55 1
2.9%
35 1
2.9%
24 1
2.9%

Other Crimes Against SCs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4562.6286
Minimum0
Maximum37653
Zeros6
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:14.982307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median148
Q34020.5
95-th percentile31792.9
Maximum37653
Range37653
Interquartile range (IQR)4014.5

Descriptive statistics

Standard deviation9840.5553
Coefficient of variation (CV)2.1567733
Kurtosis6.0147319
Mean4562.6286
Median Absolute Deviation (MAD)148
Skewness2.6373626
Sum159692
Variance96836528
MonotonicityNot monotonic
2024-01-01T21:53:15.171998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 6
 
17.1%
5 2
 
5.7%
17412 1
 
2.9%
5354 1
 
2.9%
148 1
 
2.9%
45 1
 
2.9%
34294 1
 
2.9%
84 1
 
2.9%
4227 1
 
2.9%
18 1
 
2.9%
Other values (19) 19
54.3%
ValueCountFrequency (%)
0 6
17.1%
4 1
 
2.9%
5 2
 
5.7%
7 1
 
2.9%
10 1
 
2.9%
12 1
 
2.9%
13 1
 
2.9%
18 1
 
2.9%
31 1
 
2.9%
45 1
 
2.9%
ValueCountFrequency (%)
37653 1
2.9%
34294 1
2.9%
30721 1
2.9%
17412 1
2.9%
7455 1
2.9%
5399 1
2.9%
5354 1
2.9%
4274 1
2.9%
4227 1
2.9%
3814 1
2.9%

Total_Crimes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10578.143
Minimum0
Maximum76473
Zeros4
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size408.0 B
2024-01-01T21:53:15.353532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.5
median945
Q312674.5
95-th percentile51367.6
Maximum76473
Range76473
Interquartile range (IQR)12652

Descriptive statistics

Standard deviation18900.872
Coefficient of variation (CV)1.7867855
Kurtosis4.0957825
Mean10578.143
Median Absolute Deviation (MAD)945
Skewness2.1306502
Sum370235
Variance3.5724296 × 108
MonotonicityNot monotonic
2024-01-01T21:53:15.559963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4
 
11.4%
2 2
 
5.7%
11409 1
 
2.9%
5 1
 
2.9%
7 1
 
2.9%
309 1
 
2.9%
971 1
 
2.9%
76473 1
 
2.9%
183 1
 
2.9%
18623 1
 
2.9%
Other values (21) 21
60.0%
ValueCountFrequency (%)
0 4
11.4%
2 2
5.7%
5 1
 
2.9%
7 1
 
2.9%
12 1
 
2.9%
33 1
 
2.9%
42 1
 
2.9%
50 1
 
2.9%
99 1
 
2.9%
183 1
 
2.9%
ValueCountFrequency (%)
76473 1
2.9%
55002 1
2.9%
49810 1
2.9%
43419 1
2.9%
33341 1
2.9%
24406 1
2.9%
18623 1
2.9%
18406 1
2.9%
13940 1
2.9%
11409 1
2.9%

Interactions

2024-01-01T21:53:04.264451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:22.376956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:25.008677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:27.439123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:29.681788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:32.054804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-01-01T21:53:00.783621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:03.202086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:05.665527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:23.944520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:26.432362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:28.707368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:31.098687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:33.312974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:35.566793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:38.074279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:40.442447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:42.840249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:45.018216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:47.128292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:49.242674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:51.562582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:53.737838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:56.046222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:58.261616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:00.910946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:03.310382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:05.790668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:24.077751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:26.567143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:28.835653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:31.212050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:33.430176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:35.689524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:38.195121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:40.552242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:42.950912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:45.124751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:47.234281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:49.349431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:51.668811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:53.859093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:56.156540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:58.379766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:01.039778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:03.429605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:05.901440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:24.188503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:26.675882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:28.950925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:31.321232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:33.537397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:35.961563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:38.319437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:40.650597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:43.052193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:45.225581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:47.331258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:49.452794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:51.770054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:53.970452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:56.261883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:58.489985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:01.151065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:03.537964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:06.026551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:24.311842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:26.797069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:29.069840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:31.459378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:33.652690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:36.089370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:38.456659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:40.763030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:43.169941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:45.343906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:47.444226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:49.575979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:51.889250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:54.103609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:56.387121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:58.618158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:01.268361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:03.661110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:06.140912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:24.453609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:26.919379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:29.185151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:31.566282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:33.765716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:36.198698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:38.572901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:40.865257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:43.281167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:45.453239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:47.552447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:49.689191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:51.990492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:54.234419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:56.499370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:58.739952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:01.378550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:03.776449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:06.258149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:24.583747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:27.044560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:29.308438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:31.673746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:33.884585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:36.313612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:38.700661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:40.976950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:43.403039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:45.562424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:47.658674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:49.799410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:52.100714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:54.364592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:56.615195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:58.861317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:01.493123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:03.894607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:06.385425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:24.720894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:27.173234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:29.430664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:31.808512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:33.999892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:36.444889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:38.840854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:41.110418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:43.538225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:45.680931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:47.779896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:49.931088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:52.220424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:54.501887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:56.737474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:58.993175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:01.615450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:04.016970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:06.528718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:24.867541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:27.308923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:29.547964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:31.933866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:34.122515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:36.563227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:38.970196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:41.237736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:43.670692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:45.806613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:47.898721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:50.052314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:52.341400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:54.640650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:56.856802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:52:59.109963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:01.741031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-01T21:53:04.137076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-01T21:53:15.777578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
% of Total PopulationArea (km*km)ArsonAssault on womenDacoityDensity (1/km*km)FemalesHurtKidnapping and AbductionLiteracy Rate (%)MalesMurderOther Crimes Against SCsPopulationPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActRobberySex RatioTotal_Crimes
% of Total Population1.0000.8520.8340.8400.7790.1481.0000.8530.776-0.5380.9990.8490.8721.0000.8680.7770.7620.2000.880
Area (km*km)0.8521.0000.8110.8030.724-0.2840.8520.7960.735-0.7030.8510.8170.7950.8540.7150.6860.7760.1800.769
Arson0.8340.8111.0000.9090.8580.0330.8320.9500.921-0.5350.8380.9180.9040.8340.8490.6890.9090.1110.899
Assault on women0.8400.8030.9091.0000.8030.0340.8360.9390.901-0.4590.8380.9210.9560.8390.8780.6990.8660.1480.931
Dacoity0.7790.7240.8580.8031.0000.1020.7780.8200.829-0.4380.7820.8220.8300.7790.8150.7790.9020.1550.819
Density (1/km*km)0.148-0.2840.0330.0340.1021.0000.1430.0830.0560.2120.1510.0290.1160.1400.2510.220-0.054-0.1780.188
Females1.0000.8520.8320.8360.7780.1431.0000.8510.773-0.5310.9990.8480.8691.0000.8680.7800.7610.2090.877
Hurt0.8530.7960.9500.9390.8200.0830.8511.0000.922-0.5060.8530.9390.9500.8530.9160.7560.8870.2220.951
Kidnapping and Abduction0.7760.7350.9210.9010.8290.0560.7730.9221.000-0.4910.7790.9320.9160.7750.8250.6580.9220.0950.906
Literacy Rate (%)-0.538-0.703-0.535-0.459-0.4380.212-0.531-0.506-0.4911.000-0.543-0.559-0.465-0.535-0.412-0.417-0.4620.055-0.480
Males0.9990.8510.8380.8380.7820.1510.9990.8530.779-0.5431.0000.8490.8700.9990.8680.7750.7610.1830.878
Murder0.8490.8170.9180.9210.8220.0290.8480.9390.932-0.5590.8491.0000.9510.8490.8890.7780.8910.1550.958
Other Crimes Against SCs0.8720.7950.9040.9560.8300.1160.8690.9500.916-0.4650.8700.9511.0000.8710.9250.7880.8620.1330.977
Population1.0000.8540.8340.8390.7790.1401.0000.8530.775-0.5350.9990.8490.8711.0000.8670.7760.7630.2040.879
Prevention of atrocities (POA) Act0.8680.7150.8490.8780.8150.2510.8680.9160.825-0.4120.8680.8890.9250.8671.0000.8670.8110.1880.959
Protection of Civil Rights (PCR) Act0.7770.6860.6890.6990.7790.2200.7800.7560.658-0.4170.7750.7780.7880.7760.8671.0000.6800.2450.827
Robbery0.7620.7760.9090.8660.902-0.0540.7610.8870.922-0.4620.7610.8910.8620.7630.8110.6801.0000.1430.856
Sex Ratio0.2000.1800.1110.1480.155-0.1780.2090.2220.0950.0550.1830.1550.1330.2040.1880.2450.1431.0000.144
Total_Crimes0.8800.7690.8990.9310.8190.1880.8770.9510.906-0.4800.8780.9580.9770.8790.9590.8270.8560.1441.000

Missing values

2024-01-01T21:53:06.748904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-01T21:53:07.138164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

StatePopulation% of Total PopulationMalesFemalesSex RatioLiteracy Rate (%)Area (km*km)Density (1/km*km)MurderAssault on womenKidnapping and AbductionDacoityRobberyArsonHurtPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActOther Crimes Against SCsTotal_Crimes
0Andhra Pradesh845807776.99424421464213863199367.02275045308556111023893913672481516015111741243419
1Arunachal Pradesh13837270.1171391266981593865.38837431710001000002
2Assam312055762.58159394431526613395872.197843839757579614332534131318945
3Bihar1040994528.60542781574982129591861.809416311022862841272977309452423425466381433341
4Chandigarh10554500.0958066347478781886.051149252020000060412
5Chhattisgarh255451982.11128328951271230399170.2813519118911766143113177439232426725214
6Delhi167879411.398887326780061586886.211484112971000000256610273
7Goa14585450.1273914071940597388.70370239401000011711333
8Gujarat604396924.99314912602894843291978.03196024308204325301852141153271397155539913940
9Haryana253514622.09134947341185672887975.554421257312844722111032524852311483366
StatePopulation% of Total PopulationMalesFemalesSex RatioLiteracy Rate (%)Area (km*km)Density (1/km*km)MurderAssault on womenKidnapping and AbductionDacoityRobberyArsonHurtPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActOther Crimes Against SCsTotal_Crimes
25Sikkim6105770.0532307028750789081.42709686817804239301899
26Tamil Nadu721470305.96361379753600905599680.091300585553522596251589226910845500422718623
27Tripura36739170.301874376179954196087.22104863504156011648084183
28Uttar Pradesh19981234116.501044805109533183191267.68240928828357736252225552147154889263785013429476473
29Uttarakhand100862920.835137773494851996379.6353483189506527014160619045971
30West Bengal912761157.54468090274446708895076.268875210302191001261111148309
31Andaman and Nicobar Islands3805810.0320287117771087686.6382494600000000000
32Dadra and Nagar Haveli3437090.0319376014994977476.2449169800200000057
33Daman and Diu2432470.021503019294661887.10112216900000000055
34Jammu and Kashmir125413021.046640662590064088967.162222365637000171111242